Yingqian Liu, Qian Huang, Huairui Li, Yunpeng Li, Sihan Li, R. Zhu, Qiang Fu
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According to the experimental results of modules, a kernel principal component analysis using mean fusion processing multi-channel data (AKPCA (fusion)) is selected, and a support vector machine using mean fusion processing multi-channel data (SVM (fusion)) is selected. The overall test accuracy and false negative rate of AKPCA (fusion) are 0.83 and 0.144, respectively, and the overall test accuracy and f1-score of SVM (fusion) are 0.966 and 1, respectively. The test results of AKPCA (fusion), SVM (fusion), and the proposed information integration algorithm show that the information integration algorithm successfully avoids a lack of abnormal status information and misdiagnosis. 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Therefore, an intelligent condition monitoring framework is designed, including the parallel implementation of unsupervised anomaly detection and fault diagnosis. A model preselection algorithm based on the highest validation accuracy is proposed for anomaly detection and fault diagnosis model selection among existing models. A novel information integration algorithm is proposed to fuse the output of anomaly detection and fault diagnosis. According to the experimental results of modules, a kernel principal component analysis using mean fusion processing multi-channel data (AKPCA (fusion)) is selected, and a support vector machine using mean fusion processing multi-channel data (SVM (fusion)) is selected. The overall test accuracy and false negative rate of AKPCA (fusion) are 0.83 and 0.144, respectively, and the overall test accuracy and f1-score of SVM (fusion) are 0.966 and 1, respectively. The test results of AKPCA (fusion), SVM (fusion), and the proposed information integration algorithm show that the information integration algorithm successfully avoids a lack of abnormal status information and misdiagnosis. 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引用次数: 0
摘要
基本服务水泵是必要的安全装置,负责通过海水将废热从安全壳中排出;其状态监测对于海滨核电站的安全稳定运行至关重要。然而,现有的智能方法很难直接应用于这些水泵。因此,我们设计了一个智能状态监测框架,包括无监督异常检测和故障诊断的并行实施。提出了一种基于最高验证精度的模型预选算法,用于在现有模型中选择异常检测和故障诊断模型。提出了一种新颖的信息融合算法,用于融合异常检测和故障诊断的输出结果。根据各模块的实验结果,选择了使用均值融合处理多通道数据的核主成分分析法(AKPCA(融合))和使用均值融合处理多通道数据的支持向量机(SVM(融合))。AKPCA(融合)的总体测试精度和假阴性率分别为 0.83 和 0.144,SVM(融合)的总体测试精度和 f1 分数分别为 0.966 和 1。AKPCA(融合)、SVM(融合)和所提出的信息融合算法的测试结果表明,信息融合算法成功地避免了异常状态信息的缺失和误诊。所提出的框架是实现复杂设备智能状态监测的一次有意义的尝试。
A Novel Intelligent Condition Monitoring Framework of Essential Service Water Pumps
Essential service water pumps are necessary safety devices responsible for discharging waste heat from containments through seawater; their condition monitoring is critical for the safe and stable operation of seaside nuclear power plants. However, it is difficult to directly apply existing intelligent methods to these pumps. Therefore, an intelligent condition monitoring framework is designed, including the parallel implementation of unsupervised anomaly detection and fault diagnosis. A model preselection algorithm based on the highest validation accuracy is proposed for anomaly detection and fault diagnosis model selection among existing models. A novel information integration algorithm is proposed to fuse the output of anomaly detection and fault diagnosis. According to the experimental results of modules, a kernel principal component analysis using mean fusion processing multi-channel data (AKPCA (fusion)) is selected, and a support vector machine using mean fusion processing multi-channel data (SVM (fusion)) is selected. The overall test accuracy and false negative rate of AKPCA (fusion) are 0.83 and 0.144, respectively, and the overall test accuracy and f1-score of SVM (fusion) are 0.966 and 1, respectively. The test results of AKPCA (fusion), SVM (fusion), and the proposed information integration algorithm show that the information integration algorithm successfully avoids a lack of abnormal status information and misdiagnosis. The proposed framework is a meaningful attempt to achieve the intelligent condition monitoring of complex equipment.